# Towards Robust Deep Learning with Ensemble Networks and Noisy Layers

Source: [Towards Robust Deep Learning with Ensemble Networks and Noisy Layers](<https://www.sota2.com/research/paper/68fa69111e209131cea1ff91>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:52:51.647Z

Authors: Yuting Liang, Reza Samavi

Published: 2020-07-03T06:04:02.000Z

## Abstract

In this paper we provide an approach for deep learning that protects against adversarial examples in image classification-type networks. The approach relies on two mechanisms:1) a mechanism that increases robustness at the expense of accuracy, and, 2) a mechanism that improves accuracy but does not always increase robustness. We show that an approach combining the two mechanisms can provide protection against adversarial examples while retaining accuracy. We formulate potential attacks on our approach with experimental results to demonstrate its effectiveness. We also provide a robustness guarantee for our approach along with an interpretation for the guarantee.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2007.01507>)

[PDF](<https://arxiv.org/pdf/2007.01507.pdf>)